Yichu Chen, Gu Gong, Kate Jones, Gianni Spiga
2022-11-29
library(survival)
library(KMsurv)
library(ggplot2)
library(ggpubr)
library(survminer)
library(plotly)
library(muhaz)
library(ggthemes)
library(plyr)## [1] FALSE
Take a look at correlation between symptoms and different infection types.
Is it more common for one type of infection based on type of sex (oral/anal).
## Call:
## survdiff(formula = surv_object ~ iinfct, data = std)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## iinfct=gonorrhea 140 73 54.5 6.28042 7.50617
## iinfct=chlamydia 396 135 153.0 2.12201 3.81136
## iinfct=both 341 139 139.5 0.00166 0.00278
##
## Chisq= 8.5 on 2 degrees of freedom, p= 0.01
## Call:
## coxph(formula = surv_object ~ iinfct, data = std)
##
## n= 877, number of events= 347
##
## coef exp(coef) se(coef) z Pr(>|z|)
## iinfctchlamydia -0.4202 0.6569 0.1457 -2.884 0.00393 **
## iinfctboth -0.2980 0.7423 0.1450 -2.055 0.03984 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## iinfctchlamydia 0.6569 1.522 0.4937 0.8741
## iinfctboth 0.7423 1.347 0.5587 0.9863
##
## Concordance= 0.524 (se = 0.016 )
## Likelihood ratio test= 7.93 on 2 df, p=0.02
## Wald test = 8.37 on 2 df, p=0.02
## Score (logrank) test = 8.46 on 2 df, p=0.01
cox1 <-
coxph(
surv_object ~ iinfct + marital + race + os12m + os30d +
rs12m + rs30d + abdpain + discharge + dysuria + condom +
itch + lesion + rash + lymph + vagina + dchexam + abnode +
age + yschool + npartner,
data = std
)